{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.用numpy创建一个2×2的二维数组ndarray，指定元素类型为float，命名为arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 3.],\n",
       "       [4., 5.]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr1 = np.array([[2,3],[4,5]], dtype=float)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.生成元素全为0的6×6矩阵arr2、元素全为1的6×6矩阵arr3以及6×6的单位矩阵arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.zeros((6,6))\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = np.ones((6,6))\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4 = np.eye(6)\n",
    "arr4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.运用arange函数生成生成[0, 10)区间内，步长为2的的整数序列arr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5 = np.arange(2, 10, 2)\n",
    "arr5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.生成 0~10间的等差数列arr6，元素个数为6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  4.,  6.,  8., 10.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr6 = np.linspace(0,10,6)\n",
    "arr6 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.创建一个长度为10的随机数组（每个元素都是整数）并将最大值替换为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 7 7 0 2 5 6 2 3 8]\n"
     ]
    }
   ],
   "source": [
    "a = np.random.randint(0, 10, size=10)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "b = np.argmax(a) #获取最大值的索引位置\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 7 7 0 2 5 6 2 3 0]\n"
     ]
    }
   ],
   "source": [
    "a[b] = 0 #将最大值改为0\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.计算数组a = np.array([1,2,3,2,3,4,3,4,5,6])和数组b = np.array([7,2,10,2,7,4,9,4,9,8])之间的欧式距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.529964086141668"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1,2,3,2,3,4,3,4,5,6])\n",
    "b = np.array([7,2,10,2,7,4,9,4,9,8])\n",
    "sqr = (a-b)**2\n",
    "sum_sqr = np.sum(sqr)\n",
    "distance = np.sqrt(sum_sqr)\n",
    "distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.利用seed生成一组固定的随机数np.random.seed(1)，并用此组成模拟的资金价值曲线values = np.random.randn(1000).cumsum()，请利用matplotlib作出该资金价值曲线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(1)\n",
    "values = np.random.randn(1000).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.008446252634755"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_drawdown = np.max(np.maximum.accumulate(values) - values)\n",
    "max_drawdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 附加思考，感兴趣的可以了解，用numpy计算最大回撤，并将点标识出来，此不属于任务内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "790 599\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = np.argmax(np.maximum.accumulate(values) -values)\n",
    "j = np.argmax(values[:i+1])\n",
    "print(i, j)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(values)\n",
    "plt.plot([i, j], [values[i], values[j]],\n",
    "         'o', color='Red', markersize=10)\n",
    "plt.show()"
   ]
  }
 ],
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